ML Anthology

Enhance Cross-Domain Aspect-Based Sentiment Analysis by Incorporating Commonsense Relational Structure (Student Abstract)

Yushi Zeng, Guohua Wang, Haopeng Ren, Yi Cai
AAAI 2022 pp. 13105-13106
doi:10.1609/AAAI.V36I11.21689 /aaai/2022/zeng2022aaai-enhance/

Abstract

Aspect Based Sentiment Analysis (ABSA) aims to extract aspect terms and identify the sentiment polarities towards each extracted aspect term. Currently, syntactic information is seen as the bridge for the domain adaptation and achieves remarkable performance. However, the transferable syntactic knowledge is complex and diverse, which causes the transfer error problem in domain adaptation. In our paper, we propose a domain-shared relational structure incorporated cross-domain ABSA model. The experimental results show the effectiveness of our model.

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Cite

Text

Zeng et al. "Enhance Cross-Domain Aspect-Based Sentiment Analysis by Incorporating Commonsense Relational Structure (Student Abstract)." AAAI Conference on Artificial Intelligence, 2022. doi:10.1609/AAAI.V36I11.21689

Markdown

[Zeng et al. "Enhance Cross-Domain Aspect-Based Sentiment Analysis by Incorporating Commonsense Relational Structure (Student Abstract)." AAAI Conference on Artificial Intelligence, 2022.](https://mlanthology.org/aaai/2022/zeng2022aaai-enhance/) doi:10.1609/AAAI.V36I11.21689

BibTeX

@inproceedings{zeng2022aaai-enhance,
  title     = {{Enhance Cross-Domain Aspect-Based Sentiment Analysis by Incorporating Commonsense Relational Structure (Student Abstract)}},
  author    = {Zeng, Yushi and Wang, Guohua and Ren, Haopeng and Cai, Yi},
  booktitle = {AAAI Conference on Artificial Intelligence},
  year      = {2022},
  pages     = {13105-13106},
  doi       = {10.1609/AAAI.V36I11.21689},
  url       = {https://mlanthology.org/aaai/2022/zeng2022aaai-enhance/}
}